


'''simple docstring'''
import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)




def 	_A  (       A							)  ->				str:
	lowercase						:		str   					=		checkpoints.load_tax_checkpoint(a__							)
	lowercase						:		int   					=		flatten_dict(a__							)
	return flax_params




def 	_A  (       A							)  ->				str:
	lowercase						:		Optional[int]   					=		{}

	lowercase						:		str   					=		{
	    """token_embedder""": """embeddings""",
	    """encoder_norm""": """layernorm""",
	    """kernel""": """weight""",
	    """.out""": """.output""",
	    """scale""": """weight""",
	    """embedders_0.pos_embedding""": """row_embedder.weight""",
	    """embedders_1.pos_embedding""": """column_embedder.weight""",
	}

	lowercase						:		Optional[int]   					=		{
	    """query""": """attention.query""",
	    """key""": """attention.key""",
	    """value""": """attention.value""",
	    """output.dense""": """output""",
	    """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
	    """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
	    """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
	    """mlp.""": """mlp.DenseReluDense.""",
	    """pre_mlp_layer_norm""": """mlp.layer_norm""",
	    """self_attention.o""": """self_attention.attention.o""",
	    """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
	    """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
	    """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
	    """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
	}

	for key in flax_dict.keys():
		if "target" in key:
			# remove the first prefix from the key
			lowercase						:		Dict   					=		""".""".join(key[1:]							)

			# rename the key
			for old, new in CONVERSION_MAPPING.items():
				lowercase						:		int   					=		new_key.replace(a__							,a__							)

			if "decoder" in new_key:
				for old, new in DECODER_CONVERSION_MAPPING.items():
					lowercase						:		Optional[Any]   					=		new_key.replace(a__							,a__							)

			if "layers" in new_key and "decoder" not in new_key:
				# use regex to replace the layer number
				lowercase						:		Optional[int]   					=		re.sub(r"layers_(\d+)"							,r"layer.\1"							,a__							)
				lowercase						:		Union[str, Any]   					=		new_key.replace("encoder"							,"encoder.encoder"							)

			elif "layers" in new_key and "decoder" in new_key:
				# use regex to replace the layer number
				lowercase						:		List[Any]   					=		re.sub(r"layers_(\d+)"							,r"layer.\1"							,a__							)

			lowercase						:		Dict   					=		flax_dict[key]

	lowercase						:		int   					=		{}
	# convert converted_dict into torch format
	for key in converted_dict.keys():
		if ("embed_tokens" not in key) and ("embedder" not in key):
			lowercase						:		List[str]   					=		torch.from_numpy(converted_dict[key].T							)
		else:
			lowercase						:		Dict   					=		torch.from_numpy(converted_dict[key]							)

	return converted_torch_dict




def 	_A  (       A							,A							,A=False							,A=False							)  ->				Optional[Any]:
	lowercase						:		Union[str, Any]   					=		get_flax_param(a__							)

	if not use_large:
		lowercase						:		str   					=		PixaStructVisionConfig()
		lowercase						:		Dict   					=		PixaStructTextConfig()
	else:
		lowercase						:		Optional[Any]   					=		PixaStructVisionConfig(
		    hidden_size=1_5_3_6							,d_ff=3_9_6_8							,num_attention_heads=2_4							,num_hidden_layers=1_8							)
		lowercase						:		Optional[Any]   					=		PixaStructTextConfig(hidden_size=1_5_3_6							,d_ff=3_9_6_8							,num_heads=2_4							,num_layers=1_8							)
	lowercase						:		Optional[Any]   					=		PixaStructConfig(
	    vision_config=encoder_config.to_dict()							,text_config=decoder_config.to_dict()							,is_vqa=a__							)

	lowercase						:		Optional[Any]   					=		PixaStructForConditionalGeneration(a__							)

	lowercase						:		Tuple   					=		rename_and_convert_flax_params(a__							)
	model.load_state_dict(a__							)

	lowercase						:		int   					=		AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"							)
	lowercase						:		Any   					=		PixaStructImageProcessor()
	lowercase						:		Tuple   					=		PixaStructProcessor(image_processor=a__							,tokenizer=a__							)

	if use_large:
		lowercase						:		str   					=		4_0_9_6

	lowercase						:		Optional[int]   					=		True

	# mkdir if needed
	os.makedirs(a__							,exist_ok=a__							)

	model.save_pretrained(a__							)
	processor.save_pretrained(a__							)

	print("Model saved in {}".format(a__							)							)


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				argparse.ArgumentParser()
					parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
					parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
					lowerCAmelCase   : Union[str, Any]             =				parser.parse_args()

					convert_pixastruct_original_pytorch_checkpoint_to_hf(
					    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
					)





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case				:      Optional[Any]   		=			10


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			for i in range(a__  ,	a__):
						if array[i] == target:
									return i
			return -1


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= 0
			a_						:						List[str] 							= len(a__)
			while left <= right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)

						a_						:						Dict 							= (left + right) // 3 + 1
						a_						:						Optional[Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									a_						:						int 							= one_third - 1
						elif array[two_third] < target:
									a_						:						Any 							= two_third + 1

						else:
									a_						:						Optional[int] 							= one_third + 1
									a_						:						int 							= two_third - 1
			else:
						return -1


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			if left < right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)
						a_						:						Optional[int] 							= (left + right) // 3 + 1
						a_						:						Union[str, Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(a__  ,	one_third - 1  ,	a__  ,	a__)
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1  ,	a__  ,	a__  ,	a__)
						else:
									return rec_ternary_search(one_third + 1  ,	two_third - 1  ,	a__  ,	a__)
			else:
						return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__snake_case				:      Optional[int]   		=			input("""Enter numbers separated by comma:\n""").strip()
	__snake_case				:      Optional[int]   		=			[int(item.strip()) for item in user_input.split(""",""")]
	assert collection == sorted(collection), F"List must be ordered.\n{collection}."
	__snake_case				:      int   		=			int(input("""Enter the number to be found in the list:\n""").strip())
	__snake_case				:      List[Any]   		=			ite_ternary_search(collection, target)
	__snake_case				:      Optional[int]   		=			rec_ternary_search(0, len(collection) - 1, collection, target)
	if resulta != -1:
		print(F"""Iterative search: {target} found at positions: {resulta}""")
		print(F"""Recursive search: {target} found at positions: {resulta}""")
	else:
		print("""Not found""")


